Overview

Dataset statistics

Number of variables10
Number of observations72157
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory5.5 MiB
Average record size in memory80.0 B

Variable types

Numeric10

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
x2 is highly correlated with x7High correlation
x7 is highly correlated with x2High correlation
x2 is highly correlated with x7High correlation
x7 is highly correlated with x2High correlation
x2 is highly correlated with x7High correlation
x7 is highly correlated with x2High correlation
x1 is highly correlated with x2High correlation
x2 is highly correlated with x1 and 3 other fieldsHigh correlation
x3 is highly correlated with x7 and 1 other fieldsHigh correlation
x4 is highly correlated with x2High correlation
x7 is highly correlated with x2 and 4 other fieldsHigh correlation
x8 is highly correlated with x7 and 2 other fieldsHigh correlation
x9 is highly correlated with x7 and 2 other fieldsHigh correlation
x10 is highly correlated with x2 and 4 other fieldsHigh correlation

Reproduction

Analysis started2022-03-01 05:39:29.475968
Analysis finished2022-03-01 05:39:47.663605
Duration18.19 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

x1
Real number (ℝ)

HIGH CORRELATION

Distinct3790
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.03898929373
Minimum-2.9756
Maximum2.4785
Zeros88
Zeros (%)0.1%
Negative40592
Negative (%)56.3%
Memory size563.9 KiB
2022-03-01T11:09:47.776197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-2.9756
5-th percentile-0.4233
Q1-0.1694
median-0.0283
Q30.0889
95-th percentile0.3267
Maximum2.4785
Range5.4541
Interquartile range (IQR)0.2583

Descriptive statistics

Standard deviation0.2488961446
Coefficient of variation (CV)-6.383704877
Kurtosis5.488000297
Mean-0.03898929373
Median Absolute Deviation (MAD)0.1275
Skewness0.02418344853
Sum-2813.350468
Variance0.06194929077
MonotonicityNot monotonic
2022-03-01T11:09:47.929831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0347104
 
0.1%
-0.0146102
 
0.1%
0.0083100
 
0.1%
0.0132100
 
0.1%
0.024499
 
0.1%
0.028399
 
0.1%
-0.019598
 
0.1%
-0.039698
 
0.1%
-0.041597
 
0.1%
0.046996
 
0.1%
Other values (3780)71164
98.6%
ValueCountFrequency (%)
-2.97561
< 0.1%
-2.38381
< 0.1%
-2.33641
< 0.1%
-2.25881
< 0.1%
-2.05861
< 0.1%
-2.00291
< 0.1%
-1.94091
< 0.1%
-1.88131
< 0.1%
-1.85211
< 0.1%
-1.84961
< 0.1%
ValueCountFrequency (%)
2.47851
< 0.1%
2.42241
< 0.1%
2.28131
< 0.1%
2.09281
< 0.1%
2.0841
< 0.1%
2.05961
< 0.1%
2.05271
< 0.1%
2.01271
< 0.1%
1.94291
< 0.1%
1.85791
< 0.1%

x2
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9638
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2366937245
Minimum-10.5142
Maximum7.5649
Zeros10
Zeros (%)< 0.1%
Negative26896
Negative (%)37.3%
Memory size563.9 KiB
2022-03-01T11:09:48.073820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-10.5142
5-th percentile-1.3082
Q1-0.5293
median0.355
Q30.958
95-th percentile1.5523
Maximum7.5649
Range18.0791
Interquartile range (IQR)1.4873

Descriptive statistics

Standard deviation1.017707897
Coefficient of variation (CV)4.299682633
Kurtosis4.879598735
Mean0.2366937245
Median Absolute Deviation (MAD)0.7036
Skewness-0.5529297618
Sum17079.10908
Variance1.035729363
MonotonicityNot monotonic
2022-03-01T11:09:48.211800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.892628
 
< 0.1%
0.885327
 
< 0.1%
1.04227
 
< 0.1%
0.87527
 
< 0.1%
1.048827
 
< 0.1%
0.907727
 
< 0.1%
0.360426
 
< 0.1%
0.928226
 
< 0.1%
0.598126
 
< 0.1%
0.89726
 
< 0.1%
Other values (9628)71890
99.6%
ValueCountFrequency (%)
-10.51421
< 0.1%
-10.37061
< 0.1%
-10.27341
< 0.1%
-10.17771
< 0.1%
-10.14451
< 0.1%
-10.03321
< 0.1%
-9.61431
< 0.1%
-9.5631
< 0.1%
-9.48581
< 0.1%
-9.28371
< 0.1%
ValueCountFrequency (%)
7.56491
< 0.1%
7.41061
< 0.1%
7.37111
< 0.1%
7.32031
< 0.1%
7.06881
< 0.1%
6.93461
< 0.1%
6.86181
< 0.1%
6.73491
< 0.1%
6.7291
< 0.1%
6.66991
< 0.1%

x3
Real number (ℝ)

HIGH CORRELATION

Distinct8041
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8122275425
Minimum-4.4111
Maximum3.7603
Zeros20
Zeros (%)< 0.1%
Negative21753
Negative (%)30.1%
Memory size563.9 KiB
2022-03-01T11:09:48.361548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-4.4111
5-th percentile-0.5792
Q1-0.0757
median1.0249
Q31.7212
95-th percentile2.0527
Maximum3.7603
Range8.1714
Interquartile range (IQR)1.7969

Descriptive statistics

Standard deviation0.9731434416
Coefficient of variation (CV)1.198116772
Kurtosis-0.9552484626
Mean0.8122275425
Median Absolute Deviation (MAD)0.8657
Skewness-0.2668906721
Sum58607.90278
Variance0.9470081579
MonotonicityNot monotonic
2022-03-01T11:09:48.507634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.771542
 
0.1%
-0.151941
 
0.1%
-0.072841
 
0.1%
1.909240
 
0.1%
1.802739
 
0.1%
1.800339
 
0.1%
1.812538
 
0.1%
1.85338
 
0.1%
1.714438
 
0.1%
1.775938
 
0.1%
Other values (8031)71763
99.5%
ValueCountFrequency (%)
-4.41111
< 0.1%
-3.98051
< 0.1%
-3.93951
< 0.1%
-3.88041
< 0.1%
-3.86911
< 0.1%
-3.78321
< 0.1%
-3.76221
< 0.1%
-3.73931
< 0.1%
-3.64361
< 0.1%
-3.60841
< 0.1%
ValueCountFrequency (%)
3.76031
< 0.1%
3.45651
< 0.1%
3.45121
< 0.1%
3.41991
< 0.1%
3.41751
< 0.1%
3.37161
< 0.1%
3.31591
< 0.1%
3.30811
< 0.1%
3.25731
< 0.1%
3.25291
< 0.1%

x4
Real number (ℝ)

HIGH CORRELATION

Distinct7806
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3161115295
Minimum-1411.499
Maximum1173.1567
Zeros337
Zeros (%)0.5%
Negative34961
Negative (%)48.5%
Memory size563.9 KiB
2022-03-01T11:09:48.655065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1411.499
5-th percentile-98.3276
Q1-21.1792
median0.8545
Q321.4233
95-th percentile109.8755
Maximum1173.1567
Range2584.6557
Interquartile range (IQR)42.6025

Descriptive statistics

Standard deviation86.37854929
Coefficient of variation (CV)273.2533971
Kurtosis30.50387693
Mean0.3161115295
Median Absolute Deviation (MAD)21.3013
Skewness-1.564935663
Sum22809.65963
Variance7461.253778
MonotonicityNot monotonic
2022-03-01T11:09:48.801888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0337
 
0.5%
0.671487
 
0.1%
-0.366287
 
0.1%
2.746687
 
0.1%
0.976685
 
0.1%
2.990783
 
0.1%
-0.976682
 
0.1%
2.319381
 
0.1%
1.831180
 
0.1%
1.159780
 
0.1%
Other values (7796)71068
98.5%
ValueCountFrequency (%)
-1411.4991
< 0.1%
-1397.70511
< 0.1%
-1341.55271
< 0.1%
-1317.32181
< 0.1%
-1275.69581
< 0.1%
-1243.53031
< 0.1%
-1241.3331
< 0.1%
-1165.64941
< 0.1%
-1158.44731
< 0.1%
-1139.28221
< 0.1%
ValueCountFrequency (%)
1173.15671
< 0.1%
1092.10211
< 0.1%
869.8731
< 0.1%
854.1261
< 0.1%
778.93071
< 0.1%
752.50241
< 0.1%
739.80711
< 0.1%
719.60451
< 0.1%
713.01271
< 0.1%
710.81541
< 0.1%

x5
Real number (ℝ)

Distinct4978
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.502638914
Minimum-464.0503
Maximum456.9702
Zeros307
Zeros (%)0.4%
Negative36224
Negative (%)50.2%
Memory size563.9 KiB
2022-03-01T11:09:48.946874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-464.0503
5-th percentile-62.0117
Q1-20.1416
median-0.3052
Q320.5688
95-th percentile65.6738
Maximum456.9702
Range921.0205
Interquartile range (IQR)40.7104

Descriptive statistics

Standard deviation43.09454618
Coefficient of variation (CV)85.73658939
Kurtosis7.68604623
Mean0.502638914
Median Absolute Deviation (MAD)20.2637
Skewness0.127333299
Sum36268.91612
Variance1857.139911
MonotonicityNot monotonic
2022-03-01T11:09:49.306712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0307
 
0.4%
-4.760786
 
0.1%
4.760780
 
0.1%
0.183179
 
0.1%
4.577678
 
0.1%
1.831177
 
0.1%
-0.549377
 
0.1%
4.028377
 
0.1%
-1.159777
 
0.1%
2.441477
 
0.1%
Other values (4968)71142
98.6%
ValueCountFrequency (%)
-464.05031
< 0.1%
-452.69781
< 0.1%
-390.07571
< 0.1%
-381.59181
< 0.1%
-371.15481
< 0.1%
-348.51071
< 0.1%
-344.48241
< 0.1%
-341.24761
< 0.1%
-336.8531
< 0.1%
-335.0831
< 0.1%
ValueCountFrequency (%)
456.97021
< 0.1%
421.75291
< 0.1%
420.77641
< 0.1%
409.48491
< 0.1%
405.33451
< 0.1%
400.63481
< 0.1%
392.82231
< 0.1%
383.42291
< 0.1%
382.93461
< 0.1%
365.23441
< 0.1%

x6
Real number (ℝ)

Distinct3463
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04412119356
Minimum-273.0103
Maximum651.3062
Zeros533
Zeros (%)0.7%
Negative35851
Negative (%)49.7%
Memory size563.9 KiB
2022-03-01T11:09:49.438479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-273.0103
5-th percentile-34.9731
Q1-11.4746
median0
Q311.3525
95-th percentile34.8511
Maximum651.3062
Range924.3165
Interquartile range (IQR)22.8271

Descriptive statistics

Standard deviation27.94899448
Coefficient of variation (CV)633.4596195
Kurtosis57.04156399
Mean0.04412119356
Median Absolute Deviation (MAD)11.4136
Skewness2.771312533
Sum3183.652964
Variance781.1462927
MonotonicityNot monotonic
2022-03-01T11:09:49.571338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0533
 
0.7%
-1.77135
 
0.2%
0.9766134
 
0.2%
0.5493133
 
0.2%
-1.2817131
 
0.2%
-0.9155130
 
0.2%
-3.7842129
 
0.2%
-3.9673128
 
0.2%
1.5869127
 
0.2%
0.3662127
 
0.2%
Other values (3453)70450
97.6%
ValueCountFrequency (%)
-273.01031
< 0.1%
-270.7521
< 0.1%
-267.63921
< 0.1%
-265.13671
< 0.1%
-243.95751
< 0.1%
-238.09811
< 0.1%
-237.06051
< 0.1%
-229.43121
< 0.1%
-222.90041
< 0.1%
-221.86281
< 0.1%
ValueCountFrequency (%)
651.30621
< 0.1%
645.20261
< 0.1%
628.60111
< 0.1%
625.91551
< 0.1%
601.86771
< 0.1%
583.2521
< 0.1%
564.57521
< 0.1%
558.71581
< 0.1%
550.59811
< 0.1%
529.11381
< 0.1%

x7
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct33020
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.46346776
Minimum-180
Maximum179.9945
Zeros7
Zeros (%)< 0.1%
Negative29379
Negative (%)40.7%
Memory size563.9 KiB
2022-03-01T11:09:49.706909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-180
5-th percentile-119.016
Q1-28.5645
median17.309
Q390.7965
95-th percentile149.8403
Maximum179.9945
Range359.9945
Interquartile range (IQR)119.361

Descriptive statistics

Standard deviation79.36284172
Coefficient of variation (CV)3.53297374
Kurtosis-0.1742541562
Mean22.46346776
Median Absolute Deviation (MAD)57.2278
Skewness-0.3286215476
Sum1620896.443
Variance6298.460646
MonotonicityNot monotonic
2022-03-01T11:09:49.838480image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-27.30114
 
< 0.1%
-25.29612
 
< 0.1%
102.06312
 
< 0.1%
102.238811
 
< 0.1%
103.980111
 
< 0.1%
38.95211
 
< 0.1%
103.958111
 
< 0.1%
-2.889411
 
< 0.1%
103.474711
 
< 0.1%
101.414811
 
< 0.1%
Other values (33010)72042
99.8%
ValueCountFrequency (%)
-1801
< 0.1%
-179.9781
< 0.1%
-179.9671
< 0.1%
-179.95612
< 0.1%
-179.94511
< 0.1%
-179.93961
< 0.1%
-179.93411
< 0.1%
-179.92862
< 0.1%
-179.92312
< 0.1%
-179.91761
< 0.1%
ValueCountFrequency (%)
179.99451
 
< 0.1%
179.9891
 
< 0.1%
179.98351
 
< 0.1%
179.9781
 
< 0.1%
179.95611
 
< 0.1%
179.95061
 
< 0.1%
179.93962
< 0.1%
179.92861
 
< 0.1%
179.91761
 
< 0.1%
179.89563
< 0.1%

x8
Real number (ℝ)

HIGH CORRELATION

Distinct16153
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8708897857
Minimum-132.7863
Maximum96.6577
Zeros70
Zeros (%)0.1%
Negative34307
Negative (%)47.5%
Memory size563.9 KiB
2022-03-01T11:09:49.979753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-132.7863
5-th percentile-28.3063
Q1-4.5264
median0.3186
Q36.795
95-th percentile37.7501
Maximum96.6577
Range229.444
Interquartile range (IQR)11.3214

Descriptive statistics

Standard deviation23.34626395
Coefficient of variation (CV)26.80736913
Kurtosis5.634309704
Mean0.8708897857
Median Absolute Deviation (MAD)5.5811
Skewness-0.5519484465
Sum62840.79427
Variance545.0480406
MonotonicityNot monotonic
2022-03-01T11:09:50.119292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
070
 
0.1%
1.19243
 
0.1%
-0.082442
 
0.1%
-0.137341
 
0.1%
0.159341
 
0.1%
0.071441
 
0.1%
-0.351640
 
0.1%
0.373539
 
0.1%
0.131838
 
0.1%
0.829538
 
0.1%
Other values (16143)71724
99.4%
ValueCountFrequency (%)
-132.78631
< 0.1%
-132.42921
< 0.1%
-131.98971
< 0.1%
-131.50091
< 0.1%
-131.00651
< 0.1%
-130.5451
< 0.1%
-130.1441
< 0.1%
-129.7761
< 0.1%
-129.41891
< 0.1%
-128.9961
< 0.1%
ValueCountFrequency (%)
96.65771
< 0.1%
96.61931
< 0.1%
96.54241
< 0.1%
96.49291
< 0.1%
96.4271
< 0.1%
96.38311
< 0.1%
96.30621
< 0.1%
96.21281
< 0.1%
96.19631
< 0.1%
95.97111
< 0.1%

x9
Real number (ℝ)

HIGH CORRELATION

Distinct20268
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.743400374
Minimum-180
Maximum179.989
Zeros43
Zeros (%)0.1%
Negative38181
Negative (%)52.9%
Memory size563.9 KiB
2022-03-01T11:09:50.266477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-180
5-th percentile-117.3812
Q1-9.7064
median-0.6537
Q35.1031
95-th percentile65.2302
Maximum179.989
Range359.989
Interquartile range (IQR)14.8095

Descriptive statistics

Standard deviation54.25579163
Coefficient of variation (CV)-5.568465787
Kurtosis3.610273321
Mean-9.743400374
Median Absolute Deviation (MAD)6.6907
Skewness-0.02946184842
Sum-703054.5408
Variance2943.690926
MonotonicityNot monotonic
2022-03-01T11:09:50.412221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
043
 
0.1%
0.236232
 
< 0.1%
0.214232
 
< 0.1%
3.867232
 
< 0.1%
2.411531
 
< 0.1%
3.449730
 
< 0.1%
3.878230
 
< 0.1%
-1.378830
 
< 0.1%
0.318630
 
< 0.1%
3.422229
 
< 0.1%
Other values (20258)71838
99.6%
ValueCountFrequency (%)
-1808
< 0.1%
-179.9891
 
< 0.1%
-179.9671
 
< 0.1%
-179.95611
 
< 0.1%
-179.93961
 
< 0.1%
-179.93411
 
< 0.1%
-179.91211
 
< 0.1%
-179.90662
 
< 0.1%
-179.89012
 
< 0.1%
-179.84621
 
< 0.1%
ValueCountFrequency (%)
179.9891
< 0.1%
179.98351
< 0.1%
179.9781
< 0.1%
179.95611
< 0.1%
179.90661
< 0.1%
179.90111
< 0.1%
179.81321
< 0.1%
179.79132
< 0.1%
179.70891
< 0.1%
179.67591
< 0.1%

x10
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1472
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.45050733
Minimum28.6121
Maximum38.8919
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size563.9 KiB
2022-03-01T11:09:50.583770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum28.6121
5-th percentile29.5445
Q132.4475
median35.1417
Q336.1476
95-th percentile38.3065
Maximum38.8919
Range10.2798
Interquartile range (IQR)3.7001

Descriptive statistics

Standard deviation2.702819651
Coefficient of variation (CV)0.07845514799
Kurtosis-0.8898367712
Mean34.45050733
Median Absolute Deviation (MAD)2.3265
Skewness-0.3010593016
Sum2485845.257
Variance7.305234064
MonotonicityNot monotonic
2022-03-01T11:09:50.753784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.5828306
 
0.4%
31.5856305
 
0.4%
36.1447296
 
0.4%
31.5798287
 
0.4%
36.1388276
 
0.4%
31.5886276
 
0.4%
29.9915259
 
0.4%
36.1418258
 
0.4%
31.5769257
 
0.4%
31.5916252
 
0.3%
Other values (1462)69385
96.2%
ValueCountFrequency (%)
28.61211
 
< 0.1%
28.6151
 
< 0.1%
28.61794
 
< 0.1%
28.62096
 
< 0.1%
28.62388
 
< 0.1%
28.62689
 
< 0.1%
28.629713
< 0.1%
28.632723
< 0.1%
28.635622
< 0.1%
28.638518
< 0.1%
ValueCountFrequency (%)
38.89192
 
< 0.1%
38.88891
 
< 0.1%
38.8833
 
< 0.1%
38.880110
 
< 0.1%
38.877111
 
< 0.1%
38.874222
< 0.1%
38.871326
< 0.1%
38.868334
< 0.1%
38.865441
0.1%
38.862453
0.1%

Interactions

2022-03-01T11:09:45.850006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:32.952594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:34.648216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:36.047617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:37.496014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:38.848113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:40.308257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:41.542142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:42.884384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:44.258960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:45.998692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:33.156500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:34.793720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:36.180316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:37.618425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:38.963873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:40.428650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:41.662460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:43.002500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:44.412495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:46.132368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:33.296507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:34.996781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:36.335735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:37.756685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:39.097047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:40.557228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:41.790802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:43.128025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:44.554896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:46.264638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:33.437029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:35.139616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:36.479102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:37.914967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:39.231582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:40.688684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:41.949235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:43.251956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:44.929780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:46.416572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:33.557558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:35.268633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:36.609143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:38.087920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:39.351179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:40.809085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:42.110406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:43.404806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:45.062324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:46.581554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:33.710142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:35.391952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:36.761636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:38.205436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:39.464669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:40.931432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:42.258948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:43.535044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:45.188849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:46.699570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:33.885478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:35.514930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:36.920311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:38.324887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:39.801818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:41.048388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:42.382169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:43.720276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:45.308959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:46.824537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:34.017764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:35.648716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:37.071599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:38.449227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:39.932029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:41.171085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:42.508390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:43.877720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:45.443684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:46.944191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:34.144233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:35.789430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:37.215263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:38.566365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:40.047540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:41.294771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:42.635811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:44.016006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:45.566134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:47.072944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:34.295508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:35.918731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:37.370687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:38.698007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:40.180483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:41.415356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:42.759473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:44.135406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T11:09:45.695320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-03-01T11:09:50.901914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-01T11:09:51.078730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-01T11:09:51.254414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-01T11:09:51.426220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-03-01T11:09:47.245069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-03-01T11:09:47.471176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

x1x2x3x4x5x6x7x8x9x10
00.2129-0.72171.662110.925323.0713-8.6670-34.5081-8.3386-1.093133.8534
10.2188-0.68411.70858.544924.7803-6.6528-34.3927-8.1683-1.285433.8564
20.2300-0.67771.72510.183124.2310-6.4697-34.3652-8.0035-1.477733.8652
30.2358-0.70751.6978-9.277321.4844-6.8359-34.4312-7.8662-1.658933.8564
40.2378-0.74761.6519-12.878419.2261-6.8359-34.5355-7.7454-1.823733.8534
50.2227-0.76611.6191-9.643618.9819-5.9204-34.6124-7.6245-1.983033.8623
60.1929-0.77251.6060-4.943819.6533-4.8218-34.6399-7.4927-2.136833.8711
70.1660-0.77781.6045-2.624519.5923-4.5166-34.6509-7.3553-2.285233.8534
80.1528-0.78961.6035-2.136219.2261-5.0659-34.6509-7.2235-2.439033.8534
90.1523-0.79051.6089-1.464819.3481-5.6152-34.6454-7.0972-2.598333.8593

Last rows

x1x2x3x4x5x6x7x8x9x10
721470.2461-0.37111.6851252.8687-81.237837.4146-39.528865.2423-65.396135.4594
721480.0918-0.20651.6792282.4707-49.804740.3442-35.337565.1160-63.891035.4594
721490.0200-0.05421.6909270.3857-46.630943.6401-31.289164.9841-62.407835.4476
72150-0.00440.13481.7095185.2417-35.217334.4238-28.410664.8633-61.276235.4388
72151-0.07420.22711.718880.2612-10.131836.3159-26.828664.9457-60.413835.4447
72152-0.06050.25681.768627.954118.61573.4790-26.658365.1270-60.540235.4594
72153-0.02930.30911.884832.531755.9082-48.8281-27.817465.4071-62.171635.4711
72154-0.03130.36332.026972.692960.1196-56.8237-28.800765.6763-64.055835.4653
72155-0.04790.42192.1865126.464821.5454-28.4424-28.317365.7257-64.912735.4505
72156-0.03910.41652.4741179.4434-19.5313-4.2725-26.400165.5334-64.775435.4535

Duplicate rows

Most frequently occurring

x1x2x3x4x5x6x7x8x9x10# duplicates
0-0.0389890.2366940.8122280.3161120.5026390.04412122.4634680.87089-9.743434.4505073